 Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficientsSittichoke Pookpunt and Weerakorn Ongsakul Renewable Energy, 2013, vol. 55, issue C, 266-276 Abstract: This paper proposes a binary particle swarm optimization (BPSO) with time-varying acceleration coefficients (TVAC) for solving optimal placement of wind turbines within a wind farm. The objective is to extract the maximum turbine power output in a minimum investment cost within a wind farm. The BPSO–TVAC algorithm is applied to 100 square cells test site considering uniform wind and non-uniform wind speed with variable direction characteristics. Linear wake model is used to calculate downstream wind speed. Test results indicate that BPSO–TVAC investment cost per installed power of both uniform and non-uniform wind speed with variable wind direction are lower than those obtained from genetic algorithm and evolutive algorithm, BPSO–TVIW (time-varying inertia weight factor), BPSO–RANDIW (random inertia weight factor) and BPSO–RTVIWAC (random time-varying inertia weight and acceleration coefficients), leading to maximum power extracted in a least investment cost manner. Keywords: Wind farm; Wind turbine; Placement design; Binary particle swarm optimization (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:55:y:2013:i:c:p:266-276 DOI: 10.1016/j.renene.2012.12.005 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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